def test_celsius_to_kelvins(self):
"""Ensures correct output from celsius_to_kelvins."""
these_temperatures_kelvins = (
temperature_conversions.celsius_to_kelvins(TEMPERATURES_DEG_C))
self.assertTrue(
numpy.allclose(these_temperatures_kelvins,
TEMPERATURES_KELVINS,
atol=TOLERANCE))
def vapour_pressure_to_dewpoint(vapour_pressures_pascals, temperatures_kelvins):
"""Converts each vapour pressure to dewpoint.
Source:
https://content.meteoblue.com/hu/specifications/weather-variables/humidity
:param vapour_pressures_pascals: numpy array (any shape) of vapour
pressures.
:param temperatures_kelvins: numpy array (same shape) of temperatures.
:return: dewpoints_kelvins: numpy array (same shape) of dewpoints.
"""
error_checking.assert_is_geq_numpy_array(
vapour_pressures_pascals, 0., allow_nan=True
)
error_checking.assert_is_geq_numpy_array(
temperatures_kelvins, 0., allow_nan=True
)
error_checking.assert_is_numpy_array(
temperatures_kelvins,
exact_dimensions=numpy.array(vapour_pressures_pascals.shape, dtype=int)
)
logarithms = numpy.log(
vapour_pressures_pascals / BASE_VAPOUR_PRESSURE_PASCALS
)
temperatures_deg_c = temperature_conv.kelvins_to_celsius(
temperatures_kelvins
)
numerator_coeffs = numpy.full(
temperatures_deg_c.shape, MAGNUS_DENOMINATOR_COEFF_WATER
)
numerator_coeffs[temperatures_deg_c < 0] = MAGNUS_DENOMINATOR_COEFF_ICE
numerators = numerator_coeffs * logarithms
denominator_coeffs = numpy.full(
temperatures_deg_c.shape, MAGNUS_NUMERATOR_COEFF_WATER
)
denominator_coeffs[temperatures_deg_c < 0] = MAGNUS_NUMERATOR_COEFF_ICE
denominators = denominator_coeffs - logarithms
dewpoints_deg_c = numerators / denominators
dewpoints_kelvins = temperature_conv.celsius_to_kelvins(dewpoints_deg_c)
dewpoints_kelvins[numpy.invert(numpy.isfinite(dewpoints_kelvins))] = 0.
dewpoints_kelvins[dewpoints_deg_c + numerator_coeffs < 0] = 0.
return dewpoints_kelvins
soundings.PRESSURE_COLUMN_FOR_SHARPPY_INPUT: THESE_PRESSURES_MB,
soundings.HEIGHT_COLUMN_FOR_SHARPPY_INPUT: THESE_HEIGHTS_M_ASL,
soundings.TEMPERATURE_COLUMN_FOR_SHARPPY_INPUT: THESE_TEMPERATURES_DEG_C,
soundings.DEWPOINT_COLUMN_FOR_SHARPPY_INPUT: THESE_DEWPOINTS_DEG_C,
soundings.U_WIND_COLUMN_FOR_SHARPPY_INPUT: THESE_U_WINDS_KT,
soundings.V_WIND_COLUMN_FOR_SHARPPY_INPUT: THESE_V_WINDS_KT,
soundings.IS_SURFACE_COLUMN_FOR_SHARPPY_INPUT: THESE_SURFACE_FLAGS
}
SOUNDING_TABLE_SHARPPY_ORIG = pandas.DataFrame.from_dict(THIS_SOUNDING_DICT)
# The following constants are used to test _sounding_to_sharppy_units.
MB_TO_PASCALS = 100
UNITLESS_TO_PERCENT = 100
KT_TO_METRES_PER_SECOND = 1.852 / 3.6
THESE_TEMPERATURES_KELVINS = temperature_conversions.celsius_to_kelvins(
THESE_TEMPERATURES_DEG_C)
THESE_DEWPOINTS_KELVINS = temperature_conversions.celsius_to_kelvins(
THESE_DEWPOINTS_DEG_C)
THESE_SPECIFIC_HUMIDITIES = moisture_conversions.dewpoint_to_specific_humidity(
THESE_DEWPOINTS_KELVINS, THESE_PRESSURES_MB * MB_TO_PASCALS)
THESE_RH_PERCENT = moisture_conversions.dewpoint_to_relative_humidity(
THESE_DEWPOINTS_KELVINS, THESE_TEMPERATURES_KELVINS,
THESE_PRESSURES_MB * MB_TO_PASCALS) * UNITLESS_TO_PERCENT
THESE_U_WINDS_M_S01 = KT_TO_METRES_PER_SECOND * THESE_U_WINDS_KT
THESE_V_WINDS_M_S01 = KT_TO_METRES_PER_SECOND * THESE_V_WINDS_KT
THIS_SOUNDING_DICT = {
soundings.PRESSURE_COLUMN_FOR_SHARPPY_INPUT: THESE_PRESSURES_MB,
nwp_model_utils.HEIGHT_COLUMN_FOR_SOUNDING_TABLES: THESE_HEIGHTS_M_ASL,
nwp_model_utils.TEMPERATURE_COLUMN_FOR_SOUNDING_TABLES:
THESE_TEMPERATURES_KELVINS,
def _plot_results_one_example(bwo_dict, example_index, model_metadata_dict,
use_log_scale, output_dir_name):
"""Plots results for one example.
:param bwo_dict: Dictionary read by
`backwards_optimization.read_file`.
:param example_index: Will plot results for example with this array index.
:param model_metadata_dict: Dictionary read by `neural_net.read_metafile`.
:param use_log_scale: See documentation at top of file.
:param output_dir_name: Name of output directory. Figure will be saved
here.
"""
# Housekeeping.
example_id_string = bwo_dict[bwo.EXAMPLE_IDS_KEY][example_index]
generator_option_dict = model_metadata_dict[
neural_net.TRAINING_OPTIONS_KEY]
base_example_dict = {
example_utils.SCALAR_PREDICTOR_NAMES_KEY:
generator_option_dict[neural_net.SCALAR_PREDICTOR_NAMES_KEY],
example_utils.VECTOR_PREDICTOR_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_PREDICTOR_NAMES_KEY],
example_utils.HEIGHTS_KEY:
generator_option_dict[neural_net.HEIGHTS_KEY]
}
init_example_dict = copy.deepcopy(base_example_dict)
init_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY] = (
bwo_dict[bwo.INIT_SCALAR_PREDICTORS_KEY][[example_index], ...])
init_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY] = (
bwo_dict[bwo.INIT_VECTOR_PREDICTORS_KEY][[example_index], ...])
final_example_dict = copy.deepcopy(base_example_dict)
final_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY] = (
bwo_dict[bwo.FINAL_SCALAR_PREDICTORS_KEY][[example_index], ...])
final_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY] = (
bwo_dict[bwo.FINAL_VECTOR_PREDICTORS_KEY][[example_index], ...])
diff_example_dict = copy.deepcopy(base_example_dict)
diff_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY] = (
final_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY] -
init_example_dict[example_utils.SCALAR_PREDICTOR_VALS_KEY])
diff_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY] = (
final_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY] -
init_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY])
predictor_names = (
diff_example_dict[example_utils.VECTOR_PREDICTOR_NAMES_KEY])
if example_utils.TEMPERATURE_NAME in predictor_names:
temperature_index = predictor_names.index(
example_utils.TEMPERATURE_NAME)
diff_predictor_matrix = (
diff_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY])
diff_predictor_matrix[..., temperature_index] = (
temperature_conv.celsius_to_kelvins(
diff_predictor_matrix[..., temperature_index]))
diff_example_dict[example_utils.VECTOR_PREDICTOR_VALS_KEY] = (
diff_predictor_matrix)
num_predictor_sets = len(PREDICTOR_NAMES_BY_SET)
for k in range(num_predictor_sets):
these_flags = numpy.array([
n in base_example_dict[example_utils.VECTOR_PREDICTOR_NAMES_KEY]
for n in PREDICTOR_NAMES_BY_SET[k]
],
dtype=bool)
these_indices = numpy.where(these_flags)[0]
if len(these_indices) == 0:
continue
predictor_names = [PREDICTOR_NAMES_BY_SET[k][i] for i in these_indices]
predictor_colours = [
PREDICTOR_COLOURS_BY_SET[k][i] for i in these_indices
]
# Plot initial and final values on the same set of axes.
handle_dict = profile_plotting.plot_predictors(
example_dict=init_example_dict,
example_index=0,
predictor_names=predictor_names,
predictor_colours=predictor_colours,
predictor_line_widths=numpy.full(len(these_indices),
SOLID_LINE_WIDTH),
predictor_line_styles=['solid'] * len(these_indices),
use_log_scale=use_log_scale,
handle_dict=None)
profile_plotting.plot_predictors(
example_dict=final_example_dict,
example_index=0,
predictor_names=predictor_names,
predictor_colours=predictor_colours,
predictor_line_widths=numpy.full(len(these_indices),
DASHED_LINE_WIDTH),
predictor_line_styles=['dashed'] * len(these_indices),
use_log_scale=use_log_scale,
handle_dict=handle_dict)
output_file_name = '{0:s}/{1:s}_predictor-set-{2:d}.jpg'.format(
output_dir_name, example_id_string.replace('_', '-'), k)
figure_object = handle_dict[profile_plotting.FIGURE_HANDLE_KEY]
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_object.savefig(output_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)
# Plot differences (final minus initial).
handle_dict = profile_plotting.plot_predictors(
example_dict=diff_example_dict,
example_index=0,
predictor_names=predictor_names,
predictor_colours=predictor_colours,
predictor_line_widths=numpy.full(len(these_indices),
SOLID_LINE_WIDTH),
predictor_line_styles=['solid'] * len(these_indices),
use_log_scale=use_log_scale,
handle_dict=None)
output_file_name = '{0:s}/{1:s}_predictor-set-{2:d}_diffs.jpg'.format(
output_dir_name, example_id_string.replace('_', '-'), k)
figure_object = handle_dict[profile_plotting.FIGURE_HANDLE_KEY]
print('Saving figure to: "{0:s}"...'.format(output_file_name))
figure_object.savefig(output_file_name,
dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0,
bbox_inches='tight')
pyplot.close(figure_object)